5 citations · 7 across the 4 of their papers we have counts for
5 papers · 1 filter
Post-training Model Quantization Using GANs for Synthetic Data Generation
Athanasios Masouris, Mansi Sharma, Adrian Boguszewski +3
Quantization is a widely adopted technique for deep neural networks to reduce the memory and computational resources required. However, when quantized, most models would need a sui…
Post-training deep neural network pruning via layer-wise calibration
Ivan Lazarevich, Alexander Kozlov, Nikita Malinin
We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run…
Neural Network Compression Framework for fast model inference
Alexander Kozlov, Ivan Lazarevich, Vasily Shamporov +2
In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF). It leverages recent advances…
Lightweight Network Architecture for Real-Time Action Recognition
Alexander Kozlov, Vadim Andronov, Yana Gritsenko
In this work we present a new efficient approach to Human Action Recognition called Video Transformer Network (VTN). It leverages the latest advances in Computer Vision and Natural…
Development of Real-time ADAS Object Detector for Deployment on CPU
Alexander Kozlov, Daniil Osokin
In this work, we outline the set of problems, which any Object Detection CNN faces when its development comes to the deployment stage and propose methods to deal with such difficul…